By GCN Staff

What's next for predictive analytics?

Predicative analytics, a statistical or data mining approach that determines outcomes using a series of algorithms and techniques used on both structured and unstructured data, has become a technology high in demand. Although not a new method, its ease of use, inexpensive computing power and organizations increasing amounts of data have driven its adoption.

The technology is being used for retention analysis, fraud detection, medical diagnosis and risk assessment, to name a few. Fern Halper, director of research for advanced analytics at TDWI, highlighted four trends about predicative analytics in a blog post on TDWI website.

Techniques: Although the top three predicative analytics techniques are linear regression, decision trees and clustering, others have become more widely used. These include time series data analysis, which can be applied to weather observations, stock market prices, and machine-generated data; machine learning, which can uncover previously unknown patterns in data; and ensemble modeling, in which predictions from a group of models are used to generate more accurate results.

Open source: Open source solutions enable a wide community to engage in innovation. The R language, a free software environment for data manipulation, statistics and graphics has become one of the most popular open source solutions addressing predicative analytics in the enterprise.

In-memory analytics: In-memory analytics processes data in random-access memory rather than on disk, which lets users to analyze large data sets more effectively.

Predictive analytics in the cloud: The use of the public cloud for analytics appears to be increasing. Organizations are starting to investigate the cloud for business intelligence, and users are putting their big data in the cloud where so can process real-time data as well as running predictive models on extremely large multisource data sets.